Predictive vegetation mapping in the Mediterranean context: Considerations and methodological issues

The need to map vegetation communities over large areas for nature conservation and to predict the impact of environmental change on vegetation distributions, has stimulated the development of techniques for predictive vegetation mapping. Predictive vegetation studies start with the development of a model relating vegetation units and mapped physical data, followed by the application of that model to a geographic database and over a wide range of spatial scales. This field is particularly important for identifying sites for rare and endangered species and locations of high biodiversity such as many areas of the Mediterranean Basin. The potential of the approach is illustrated with a mapping exercise in the alti-meditterranean zone of Lefka Ori in Crete. The study established the nature of the relationship between vegetation communities and physical data including altitude, slope and geomorphology. In this way the knowledge of community distribution was improved enabling a GIS-based model capable of predicting community distribution to be constructed. The paper describes the development of the spatial model and the methodological problems of predictive mapping for monitoring Mediterranean ecosystems. The paper concludes with a discussion of the role of predictive vegetation mapping and other spatial techniques, such as fuzzy mapping and geostatistics, for improving our understanding of the dynamics of Mediterranean ecosystems and for practical management in a region that is under increasing pressure from human impact.

Vegetation mapping in an area of Ombrophilous Dense Forest at Parque Estadual da Serra do Mar, São Paulo State, Brazil, and floristic composition of the tree component of some physiognomies*

This study aimed to map phytophysiognomies of an area of Ombrophilous Dense Forest at Parque Estadual da Serra do Mar and characterize their floristic composition. Photointerpretation of aerial photographs in scale of 1:35,000 was realized in association with field work. Thirteen physiognomies were mapped and they were classified as Montane Ombrophilous Dense Forest, Alluvial Ombrophilous Dense Forest or Secondary System. Three physiognomies identified at Casa de Pedra streamlet's basin were studied with more details. Riparian forest (RF), valley forest (VF), and hill forest (HF) presented some floristic distinction, as confirmed by Detrended Correspondence Analysis (DCA) and Indicator Species Analysis (ISA) conducted here. Anthropic or natural disturbances and heterogeneity of environmental conditions may be the causes of physiognomic variation in the vegetation of the region. The results presented here may be useful to decisions related to management and conservation of Núcleo Santa Virgínia forests, in general.

Mapping indicators of riparian vegetation health using IKONOS and Landsat-7 ETM+ image data in Australian tropical savannas

Government agencies responsible for riparian environments are assessing the utility of remote sensing for mapping and monitoring environmental health indicators. The objective of this work was to evaluate IKONOS and Landsat-7 ETM+ imagery for mapping riparian vegetation health indicators in tropical savannas for a section of Keelbottom Creek, Queensland, Australia. Vegetation indices and image texture from IKONOS data were used for estimating percentage canopy cover (r2=0.86). Pan-sharpened IKONOS data were used to map riparian species composition (overall accuracy=55%) and riparian zone width (accuracy within 4 m). Tree crowns could not be automatically delineated due to the lack of contrast between canopies and adjacent grass cover. The ETM+ imagery was suited for mapping the extent of riparian zones. Results presented demonstrate the capabilities of high and moderate spatial resolution imagery for mapping properties of riparian zones, which may be used as riparian environmental health indicators

Evaluation of aerial remote sensing techniques for vegetation management in power line corridors

The following paper presents an evaluation of airborne sensors for use in vegetation management in powerline corridors. Three integral stages in the management process are addressed including, the detection of trees, relative positioning with respect to the nearest powerline and vegetation height estimation. Image data, including multi-spectral and high resolution, are analyzed along with LiDAR data captured from fixed wing aircraft. Ground truth data is then used to establish the accuracy and reliability of each sensor thus providing a quantitative comparison of sensor options. Tree detection was achieved through crown delineation using a Pulse-Coupled Neural Network (PCNN) and morphologic reconstruction applied to multi-spectral imagery. Through testing it was shown to achieve a detection rate of 96%, while the accuracy in segmenting groups of trees and single trees correctly was shown to be 75%. Relative positioning using LiDAR achieved a RMSE of 1.4m and 2.1m for cross track distance and along track position respectively, while Direct Georeferencing achieved RMSE of 3.1m in both instances. The estimation of pole and tree heights measured with LiDAR had a RMSE of 0.4m and 0.9m respectively, while Stereo Matching achieved 1.5m and 2.9m. Overall a small number of poles were missed with detection rates of 98% and 95% for LiDAR and Stereo Matching.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.

Queensland Coastal Wetlands Resources Mapping Data

1:100,000 coastal wetland vegetation mapping for Queensland including mangrove communities, saltpans and saline grasslands. Mapping taken from Landsat TM images with ground truthing. Additional metadata is available for details of techniques and accuracy for each section of coastline. Data Currency for each section of coast: NT border to Flinders River - 1995 SE Gulf of Carpentaria - 1987, 1988, 1991, 1992 Cape York Peninsula - 1986-88, 1991 Cape Trib to Bowling Green Bay - 1997-99 The Burdekin Region - 1991 The Bowen Region - 1994-95 The Whitsunday Region - 1997 Repulse Bay - 1989 Central Qld - 1995, 1997 The Curtis Coast Region - 1997 Round Hill Head to Tin Can Inlet - 1997 Moreton Region - 1995. Article Links: 1/ #1662. Queensland Coastal Wetland Resources: the Northern Territory Border to Flinders River. Project Report. Information Series QI00099. 2/ #1663. Queensland Coastal Wetland Resources: Sand Bay to Keppel Bay. Project Report. Information Series QI00100. 3/ #1664. Queensland Coastal Wetland Resources: Cape Tribulation to Bowling Green Bay. Project Report. Information Series QI01064. 4/ #1666. Coastal Wetlands Resources Investigation of the Burdekin Delta for declaration as fisheries reserves. Report to Ocean Rescue 2000. Project Report. 5/ #1667. Queensland Coastal Wetland Resource Investigation of the Bowen Region: Cape Upstart to Gloucester Island. Project Report. 6/ #1784. Resource Assessment of the Tidal Wetland Vegetation of Western Cape York Peninsula, North Queensland, Report to Ocean Rescue 2000. Project Report. 7/ #1785. Marine Vegetation of Cape York Peninsula. Cape York Peninsula Land Use Strategy. Project Report. 8/ #3544. Queensland Coastal Wetland Resources: The Whitsunday Region. Project Report.Information Series QI01065. 9/ #3545. Queensland Coastal Wetland Resources: Round Hill Head to Tin Can Inlet. Project Report. Information Series QI99081.

Integrating JERS-1 imaging radar and elevation models for mapping tropical rainforest communities in far North Queensland, Australia

The Wet Tropics World Heritage Area in Far North Queens- land, Australia consists predominantly of tropical rainforest and wet sclerophyll forest in areas of variable relief. Previous maps of vegetation communities in the area were produced by a labor-intensive combination of field survey and air-photo interpretation. Thus,. the aim of this work was to develop a new vegetation mapping method based on imaging radar that incorporates topographical corrections, which could be repeated frequently, and which would reduce the need for detailed field assessments and associated costs. The method employed G topographic correction and mapping procedure that was developed to enable vegetation structural classes to be mapped from satellite imaging radar. Eight JERS-1 scenes covering the Wet Tropics area for 1996 were acquired from NASDA under the auspices of the Global Rainforest Mapping Project. JERS scenes were geometrically corrected for topographic distortion using an 80 m DEM and a combination of polynomial warping and radar viewing geometry modeling. An image mosaic was created to cover the Wet Tropics region, and a new technique for image smoothing was applied to the JERS texture bonds and DEM before a Maximum Likelihood classification was applied to identify major land-cover and vegetation communities. Despite these efforts, dominant vegetation community classes could only be classified to low levels of accuracy (57.5 percent) which were partly explained by the significantly larger pixel size of the DEM in comparison to the JERS image (12.5 m). In addition, the spatial and floristic detail contained in the classes of the original validation maps were much finer than the JERS classification product was able to distinguish. In comparison to field and aerial photo-based approaches for mapping the vegetation of the Wet Tropics, appropriately corrected SAR data provides a more regional scale, all-weather mapping technique for broader vegetation classes. Further work is required to establish an appropriate combination of imaging radar with elevation data and other environmental surrogates to accurately map vegetation communities across the entire Wet Tropics.

Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: A case study from the wet tropics of Northeastern Australia

Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.

Uso de imagens de satélite de alta resolução (GeoEye-1) para estimativa de acúmulo de carbono em alagados de Mata Atlântica em recuperação na Reserva Ecológica Guapiaçu (Cachoeiras de Macacu, Rio de Janeiro)

Áreas alagadas são importantes devido à grande biodiversidade que sustentam e aos serviços ambientais gerados pela sua conservação. Essas áreas, quando dominadas por macrófitas, tendem a suportar grande biodiversidade e assumir grande valor de conservação. Assim, o monitoramento do estabelecimento deste importante componente do ecossistema durante um projeto de recuperação de ecossistemas é importante para avaliar o sucesso da sua recuperação. Este trabalho teve como objetivo estimar aquantidade de biomassa por área acumulada em um ecossistema ao longo de um gradiente de recuperação. Através da classificação não supervisionada gerada a partir de de imagens de satélite de alta resolução (GeoEye-1) e amostragem destrutiva foram estimadas quantidades de biomassa por área em três alagados em recuperação na Reserva Ecológica Guapiaçú. A classificação não supervisionada se mostrou uma ferramenta acurada e eficiente no mapeamento de classes de vegetação. Os alagados estudados apresentam uma taxa de acúmulo de carbono anual estimada em 1,12 MgC.hec-1 atingindo um máximo de 5.55 MgC.hec-1 no terceiro ano. Adicionalmente, foi observada uma correlação negativa entre biomassa e profundidade.

东灵山地区植被图的编制

本论文充分利用野外调查的结果、地形底图、航空照片和收集到的各种文字、图件等资料，编制了东灵山地区北京森林站1:20,000在比例尺现状植被图，对图例进行了简要说明。综合利用群落生态学原则和多元分析、种间相关等数量分析方法，进行了群落分类并确定了分类和图例系统，这是植被制图方法的一种尝试。并利用EPPL7地理信息系统软件，绘制了北京森林站的系列彩色图（1:50,000植被图，1:50,000土壤图、1:65,000地质图等）。

神农架的植被及其1：200，200植被图的编制

神农架位于湖北省西部，长江以北、汉水以南的广阔地带，属北亚热带向暖温带的过渡区域。本文依据该地区所处地理位置的植被分布规律等资料，绘制了1：20万的植被复原图。并在此基础上，运用ERDAS imagine 8.4和Maplnfoprofessional 6.0软件，分别对神农架地区的TM影像（5、4、3波段）进行监督分类及目视解译，同时结合野外的样方调查，绘制了神农架地区1：20万的植被类型图，并建立了相应的属性数据库。最后，根据野外的GPS定位点对制图精度进行了Kappa检验。 制图结果表明，制图区总面积3476.67 km2，共计504个斑块。据统计，林地面积2607.45 km2，森林覆盖率75%;山地灌丛及亚高山灌丛总面积358.62 km2，占总面积的10.3%：草甸面积156.84 km2，占4.51%。自然植被划分为10个植被型，46个群系，以及农田（包括居民点）和茶园两种农业土地利用类型。其中针叶、落叶阔叶混交林面积最大（6个群系），约908 km2，占总面积的26.12%;其它依次为落叶阔叶林（1 1个群系），针叶林（4个群系），常绿阔叶林（3个群系），山地灌丛（5个群系），常绿阔叶、落叶阔叶混交林（3个群系），亚高山灌丛(6个群系)，草甸（4个群系）以及亚高山针叶林（3个群系）等。另外，两种农业土地利用类型面积共计430 kn12，占总面积的12.37%。 植被类型图与复原图叠加分析表明：①常绿阔叶林的理论分布区域，由常绿阔叶林，常绿、落叶阔叶混交林等7种植被型以及农田（含居民点）等土地利用类型共同组成。因处低海拔区域，人口集中，所以农田（含居民点）分布最广，所占面积最大，占到该区域面积的35.28%;加上长期的人为干扰，常绿阔叶林面积缩小至48.76 krr12，占到该区域面积的13.93%;②常绿、落叶阔叶混交林的理论分布区域内，因干扰后落叶阔叶林恢复较快，逐渐占据优势。另外，该区域海拔较低，人类活动也较频繁，农田（含居民点）面积仍有相当的比例;③针阔混交林理论分布区海拔位置高，人为活动影响少，原地带性植被保存较好，分布面积最大;其余部分为落阔林等7种植被型共同组成。④针叶林理论分布区域应是以巴山冷杉林为单优种的亚高山针叶林带，但因历史上的皆伐及火烧等原因，现面积仅有17 kfr12，占该区域的19.8%，其余则为亚高山灌丛及亚高山草甸所替代。